We handle every stage of AI integration: the strategy that decides what is worth doing, the training that gets people using it, the engineering that builds what does not exist yet, and the follow-through that makes sure your team is still using it three months later.
Thirty minutes with the strategist who would also build it, or a free AI scan first if you want to see the opportunity in writing.
Strategic AI delivery
Most businesses know they should be using AI and cannot see the route from here to there. We take you from that uncertainty to impact you can actually measure.
We work through the business one team at a time, then build the workflows and agents that pay real money back
Financial Services
High impactBusiness impact
$380K/yr
Ops + Compliance
3 month payback
Insurance
High impactBusiness impact
$410K/yr
Claims + Underwriting
2 month payback
We choose and configure the models that fit your stack, your data and your budget, then prove the choice against your own work
We teach your team where AI earns its place, where it does not, and how to tell the difference without us in the room
Strategy through to the systems your staff open on a Tuesday morning. Most engagements draw on several of these at once, and very few need all six.
A prioritised view of where AI is worth applying across your business, what each option would take to deliver, and which ideas to leave alone.
Hands-on training built around your own workflows, so staff learn on the work in front of them instead of on generic examples, and someone internal is left able to carry it on.
Purpose-built assistants, agents and internal tools shaped around your processes, your data and your rules, integrated into the systems your team already opens every morning.
End-to-end automation of the routine path through a process (the documents, queues, approvals and handoffs), with people kept on the decisions that genuinely need judgement.
The connective work everything else depends on: integrations between your systems, pipelines that keep data current, and getting your information into a state software can actually use.
Practical policy, access controls and review processes that let people use AI on real work without putting client data, contractual obligations or compliance at risk.
An AI system that lives in its own separate tool gets used twice and then forgotten. These are the kinds of systems we read from and write back into, so the work shows up where your team already looks.
It starts with strategy, because the most expensive AI mistake is building the wrong thing well. Each stage is scoped on its own and produces something useful by itself, so you can stop after any one of them and keep what it produced.
Most companies do not have an AI problem so much as a prioritisation problem. We go through how the business actually runs and come back with a ranked view of where AI would pay, what each option would take to deliver, and which ideas are best left alone. What you get is a plan specific enough to act on and honest enough to include the things we think you should not build.
How we choose the first project
Buying tools does not change how a company works; people and process do. This is training built on your own workflows rather than generic examples, the data and integration groundwork that quietly stalls most projects, and clear rules about what staff are and are not allowed to put into these systems. It is the stage most often skipped, and it is the most common reason nothing sticks.
How we bring teams up to speed
Whatever the plan calls for (something custom built, a process automated, or software you already pay for finally configured properly), taken all the way into daily use rather than stopping at a prototype that impresses a steering committee. We agree how success will be measured and record where you are starting from, so what you get at the end is a number rather than a story.
What we build and integrateThis is one example of one capability: document extraction, running for real rather than staged. It is nowhere near the depth of an actual engagement, which is built specifically around your business and your industry. Try a sample document, or upload one of your own.
Document extraction
Or use your own. Drop a document here, or choose a file. PDF, PNG, JPEG, or text, up to 4 MB.
Your file is sent to Anthropic’s API for this one request, is never stored here, and is not used for training. It is still a public demo, so please don’t upload anything you could not email to a stranger. The samples above exist for exactly that reason.
NORTHRIDGE SUPPLY CO.
1420 Commerce Way, Suite 300
Akron, OH 44311
INVOICE
Invoice #: NRS-2026-04817
Date: 14 July 2026
Terms: Net 30
Due: 13 August 2026
Bill To:
Halloway Mechanical Contracting
Attn: Accounts Payable
88 Foundry Street
Youngstown, OH 44503
PO Reference: HMC-PO-99312
Description Qty Unit Amount
3/4" copper elbow, type L 240 $3.85 $924.00
Ball valve, brass, 1" 60 $18.40 $1,104.00
Pipe hanger assembly 150 $6.20 $930.00
Threaded rod, 1/2" x 10' 40 $11.75 $470.00
Freight $185.00
Subtotal $3,613.00
Tax (6.75%) $243.88
TOTAL $3,856.88
Remit to: Northridge Supply Co., Acct 8841-22019, Routing 041215663
Questions: ar@northridgesupply.exampleLine items (5)
| 3/4" copper elbow, type L | 240 | $924.00 |
| Ball valve, brass, 1" | 60 | $1,104.00 |
| Pipe hanger assembly | 150 | $930.00 |
| Threaded rod, 1/2" x 10' | 40 | $470.00 |
| Freight | — | $185.00 |
Flagged for a human
No confidence score here is the model’s own. Every value is checked against the document it came from, and anything that does not check out is handed to a person.
A stored result, so there is nothing to wait for. Run it to see the real thing.
Answered here so that the call itself can be about your business rather than about how we work.
It depends on what the problem turns out to need. Sometimes it is custom software running in your environment. Sometimes it is tools you already pay for, configured properly, with your team trained to use them. Often it is a prioritised plan plus the first piece of it delivered. What every engagement includes is before-and-after numbers on whatever we changed, and something people are still using in six months.
They probably can, and if they have shipped an AI system to production before and have the room to focus, that is the cheaper path. What sinks most internal attempts is not ability, it is that the work happens in the gaps between everyone's real job. The parts that decide whether a system survives contact with users (evaluation, failure handling, the boring integration work) are the first things to get cut when the sprint fills up.
Almost never, and we would push back if someone suggested it. We build into what you already run. If a tool you are already paying for has an AI feature that solves the problem, the recommendation will be to switch it on rather than to bill you for rebuilding it.
Enough depends on scope that a published number would mislead you more than it helped. Engagements open with a short fixed-price piece of discovery, so the first invoice is small and walking away afterwards is easy. You get an exact figure in writing before any build starts, and the discovery fee comes off that figure if you go ahead.
Anyone wanting a chatbot bolted onto a marketing site. Anyone who cannot yet describe what a good outcome would look like, because we would just be guessing expensively on your behalf. Anyone shopping purely on hourly rate. And anyone who needs a deck for a board meeting rather than a system that runs, which is a real need but not the one we serve.
That depends on the scope, which is why there is no number on this page. One well-defined workflow and a rollout across a division are not the same project, and quoting the first would misrepresent the second. You get a timeline for your specific scope before you commit to it, and you hear from us early if it moves rather than at the end. What does not vary is that we agree how success will be measured before anything gets built, and record where you are starting from, so you finish with a comparison rather than a story.
You do, entirely. Code in your repositories, models running on your own API keys, documentation aimed at whoever picks it up next. Nothing depends on us continuing to exist and there is no license to keep paying.
Then that is what we will tell you, in writing. A fair number of problems that arrive labelled as AI problems turn out to be a reporting query, a form, or a process that nobody has looked at in four years. Saying so costs us one project and tends to earn the next one.
Bring whatever is on your mind, whether that is a process that annoys you or a board that keeps asking questions. In half an hour you will know whether there is something here worth doing, roughly what it would take, and whether we are the right people for it.